Practice of therapy acquired regulatory skills and depressive relapse/recurrence prophylaxis following cognitive therapy or mindfulness based cognitive therapy.
Bibliographic record
Abstract
BACKGROUND: To investigate whether usage of treatment-acquired regulatory skills is associated with prevention of depressive relapse/recurrence. METHOD: Remitted depressed outpatients entered a 24-month clinical follow up after either 8 weekly group sessions of cognitive therapy (CT; N = 84) or mindfulness-based cognitive therapy (MBCT; N = 82). The primary outcome was symptom return meeting the criteria for major depression on Module A of the SCID. RESULTS: Factor analysis identified three latent factors (53% of the variance): decentering (DC), distress tolerance (DT), and residual symptoms (RS), which were equivalent across CT and MBCT. Latent change score modeling of factor slopes over the follow up revealed positive slopes for DC (β = .177), and for DT (β = .259), but not for RS (β = -.017), indicating posttreatment growth in DC and DT, but no change in RS. Cox regression indicated that DC slope was a significant predictor of relapse/recurrence prophylaxis, Hazard Ratio (HR) = .232 90% Confidence Interval (CI) [.067, .806], controlling for past depressive episodes, treatment group, and medication. The practice of therapy-acquired regulatory skills had no direct effect on relapse/recurrence (β = .028) but predicted relapse/recurrence through an indirect path (β = -.125), such that greater practice of regulatory skills following treatment promoted increases in DC (β = .462), which, in turn, predicted a reduced risk of relapse/recurrence over 24 months (β = -.270). CONCLUSIONS: Preventing major depressive disorder relapse/recurrence may depend upon developing DC in addition to managing residual symptoms. Following the acquisition of therapy skills during maintenance psychotherapies, DC is strengthened by continued skill utilization beyond treatment termination. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".